摘要
风电机组状态的准确监测对风电机组安全稳定运行和经济效益提升至关重要。但是,受不同风电机组运行数据分布差异的影响,现有状态监测方法在多风电机组应用场景下存在精度和效率难以兼顾的问题,而平衡分布自适应迁移学习(BDA)可以拉近数据距离,同化数据分布。因此,文章提出了一种基于BDA的多风电机组状态监测方法。首先,基于Copula熵的互信息法挖掘风电机组运行状态关键影响参量;然后,构建基于门控循环单元模型(GRU)和序贯概率比检验(SPRT)方法的单风电机组状态监测模型;最后,构建基于BDA的多风电机组运行数据分布同化模型,并用于多风电机组运行状态监测。算例结果表明,所提方法可以有效节省建模成本和计算成本,能够在保障多风电机组运行状态监测精度的前提下,显著提升监测效率。
Accurate condition monitoring of wind turbines is crucial to the safe and stable operation of wind turbines and the improvement of economic benefits.However,affected by the divergence in the distribution of operating data of different wind turbines,the existing condition monitoring methods have the problem of difficulty in taking into account the accuracy and efficiency in the application scenario of multiple wind turbines.BDA can shorten the data distance and reduce the data distribution divergence.Therefore,this paper propose a multi-wind turbine condition monitoring method based on balanced distribution adaptive transfer learning.Firstly,the mutual information method based on Copula entropy is used to mine the key influencing parameters of the wind turbine condition;then,a wind turbine condition monitoring model is established based on the GRU model and SPRT method;wind turbine operation data distribution assimilation model based on BDA is constructed,and used for multi-wind turbine condition monitoring.Results show that the proposed method can effectively save the modeling cost and calculation cost,and can significantly improve the monitoring efficiency on the premise of ensuring the monitoring accuracy of the operating state of multiple wind turbines.
作者
张雅洁
王罗
刘宇璐
乐波
韩爽
苏营
刘永前
Zhang Yajie;Wang Luo;Liu Yulu;Yue Bo;Han Shuang;Su Ying;Liu Yongqian(State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources(North China Electric Power University),Beijing 102206,China;School of New Energy,North China Electric Power University,Beijing 102206,China;China Three Gorges Corporation,Beijing 100038,China)
出处
《可再生能源》
CAS
CSCD
北大核心
2024年第8期1068-1073,共6页
Renewable Energy Resources
基金
中国长江三峡集团有限公司企业科技项目(212103368)。
关键词
风电机组
状态监测
平衡分布自适应迁移学习
序贯概率比检验
门控循环单元
wind turbine
condition monitor
balanced distribution adaptive transfer learning
sequential probability ratio test
gated recurrent unit
